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Information Technology Journal
Year: 2013  |  Volume: 12  |  Issue: 23  |  Page No.: 7574 - 7579

Classification of Lettuce Nitrogen Levels Based on Image Feature Extraction and Optimization

Sun Jun, Jiang Shuying, Mao Hanping, Zhang Xiaodong, Zhu Wenjing and Wang Yan    

Abstract: The feature extraction and optimization of lettuce leaf image are the important premise of classification recognition of lettuce nitrogen levels. The lettuce samples of different nitrogen levels were cultivated in soilless cultivation using nitrogen nutrition of different concentrations. When the lettuce leaf images were collected, image features have been extracted, including texture features, shape features and color features. Because of the redundancy of characteristic values, there were influences in the accuracy and efficiency of image recognition. Genetic algorithm was used to optimize 11 eigenvalues and the Principal Component Analysis (PCA) dimension reduction method was used to choose 12 principal component feature values whose cumulative contribution rate reached 98.24%. Later, the Support Vector Machine (SVM) was used as classifier. The 90 samples were chosen as training samples and the remaining 30 samples were chosen as the test samples. The result shows that, the prediction accuracy of SVM classifier based on genetic algorithm feature optimization reaches 93.33% and that based on PCA features optimization reaches 76.67%. So the genetic algorithm feature optimization is more suitable for lettuce leaf image feature optimization.

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